Literature on transportation planning and modeling is replete with the concept of a travel time budget. According to this concept, average daily travel times tend to be relatively constant. However, evidence from the 1983 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey suggest that the average daily travel time has increased by 1.9 minutes per year. Understanding travel time expenditures is important for forecasting travel demand, especially future vehicle miles of travel. Historically, travel demand models considered vehicle availability and income as limiting factors for travel, but going forward time may be the constraint. As individuals spend more time devoted to travel, less time will be available for other activities. Therefore, future travel demand is dependent on people's willingness to spend time traveling. Growth of travel demand has been per capita based not just population based. This has been enabled by several cultural trends, including fewer children to care for; specialization of activities; multitasking during travel, for example, cell phone use can add value to travel time; seeking socialization away from home; and increases in real income enabling more activity participation. This study will report the increase in average daily travel time expenditures and analyze the increase by various demographic segments of the population. Travel time expenditures are also related to activity participation, the characteristics of the area, and many other interrelated factors at the person level. Aggregate values will be used to investigate the general relationships between daily travel time expenditures and socio-demographic characteristics. Careful consideration of the implications of the increase in travel time, as well as the changes in society that have contributed to these changes will be explored. The increase in travel time expenditures is likely to play a significant role in future travel demand growth in the United States and will impact the performance of the transportation system going forward. If travel time expenditures continue to grow, the hope for slowing VMT growth may not materialize. Understanding the mechanics of why people are traveling more will aid planners and modelers in estimating future travel demand.

Over the last few decades there has been a gradual transformation in both the spatial and temporal patterns of urban activities. The percentage share of non-discretionary travel such as morning rush-hour commuting has been declining with the increased income level. Discretionary activities appear to rise prominently in urban business and leisure hubs, attracting large volumes of crowds which in turn imply new and changed demand for building floorspace and urban infrastructure. Despite impressive advances in the theories and models of infrastructure demand forecasting, there appear to be an apparent research gap in addressing the practical needs of infrastructure planning in and around those growing urban activity hubs. First, land use and transport interaction models which have to date been the mainstay of practical policy analytics tend to focus on non-discretionary activities such as rush-hour commuting. Secondly, the emerging activity based models, while providing significant new insights into personal, familial activities, especially the discretionary travel, are so data hungry and computing intensive that they have not yet found their roles in practical policy applications. This dissertation builds on the insights from above schools of modelling to develop a new approach that addresses the infrastructure planning needs of the growing urban hubs while keeping the data and computing realistic in medium to high income cities. The new model is designed based on an overarching hypothesis that considerable efficiency and welfare gains can be achieved in the planning and development of urban business and leisure hubs if the infrastructure provisions for discretionary and non-discretionary activities can be coordinated. This is a research theme that has been little explored in current literature. The new infrastructure demand forecasting model has been designed with regard to the above hypothesis and realistic data availability, including those emerging online. The model extends the framework of land use transport interaction models and aim to provide a practical modelling tool. Land use changes are accounted for when testing new infrastructure investment initiatives and especially the road and public transport loads are assessed throughout all time periods of a working day. The new contribution to the modelling methodology includes the extension to the land use transport interaction framework, the use of social media data for estimating night market activity distribution and a rapid estimation of road traffic speeds from Google directions API, and model validation. Another new contribution is the understanding of the nature and magnitude of future infrastructure demand through assessing three alternative land use scenarios: (1) business as usual, (2) inner city regeneration for a major business hub around the night market, and (3) dispersed suburban growth with distant subcentres. The model is able to assess the implications for future infrastructure demand and user welfare through discerning the distinct discretionary and non-discretionary activity patterns.